def eval_one_result(loader, folder, one_mask_per_image=False, mask_thres=0.5, use_void_pixels=True, custom_box=False):
def mAPr(per_cat, thresholds):
n_cat = len(per_cat)
all_apr = np.zeros(len(thresholds))
for ii, th in enumerate(thresholds):
per_cat_recall = np.zeros(n_cat)
for jj, categ in enumerate(per_cat.keys()):
per_cat_recall[jj] = np.sum(np.array(per_cat[categ]) > th)/len(per_cat[categ])
all_apr[ii] = per_cat_recall.mean()
return all_apr.mean()
# Allocate
eval_result = dict()
eval_result["all_jaccards"] = np.zeros(len(loader))
eval_result["all_percent"] = np.zeros(len(loader))
eval_result["meta"] = []
eval_result["per_categ_jaccard"] = dict()
# Iterate
for i, sample in enumerate(loader):
if i % 500 == 0:
print('Evaluating: {} of {} objects'.format(i, len(loader)))
# Load result
if not one_mask_per_image:
filename = os.path.join(folder,
sample["meta"]["image"][0] + '-' + sample["meta"]["object"][0] + '.png')
else:
filename = os.path.join(folder,
sample["meta"]["image"][0] + '.png')
mask = np.array(Image.open(filename)).astype(np.float32) / 255.
gt = np.squeeze(helpers.tens2image(sample["gt"]))
if use_void_pixels:
void_pixels = np.squeeze(helpers.tens2image(sample["void_pixels"]))
if mask.shape != gt.shape:
mask = cv2.resize(mask, gt.shape[::-1], interpolation=cv2.INTER_CUBIC)
# Threshold
mask = (mask > mask_thres)
if use_void_pixels:
void_pixels = (void_pixels > 0.5)
# Evaluate
if use_void_pixels:
eval_result["all_jaccards"][i] = evaluation.jaccard(gt, mask, void_pixels)
else:
eval_result["all_jaccards"][i] = evaluation.jaccard(gt, mask)
if custom_box:
box = np.squeeze(helpers.tens2image(sample["box"]))
bb = helpers.get_bbox(box)
else:
bb = helpers.get_bbox(gt)
mask_crop = helpers.crop_from_bbox(mask, bb)
if use_void_pixels:
non_void_pixels_crop = helpers.crop_from_bbox(np.logical_not(void_pixels), bb)
gt_crop = helpers.crop_from_bbox(gt, bb)
if use_void_pixels:
eval_result["all_percent"][i] = np.sum((gt_crop != mask_crop) & non_void_pixels_crop)/np.sum(non_void_pixels_crop)
else:
eval_result["all_percent"][i] = np.sum((gt_crop != mask_crop))/mask_crop.size
# Store in per category
if "category" in sample["meta"]:
cat = sample["meta"]["category"][0]
else:
cat = 1
if cat not in eval_result["per_categ_jaccard"]:
eval_result["per_categ_jaccard"][cat] = []
eval_result["per_categ_jaccard"][cat].append(eval_result["all_jaccards"][i])
# Store meta
eval_result["meta"].append(sample["meta"])
# Compute some stats
eval_result["mAPr0.5"] = mAPr(eval_result["per_categ_jaccard"], [0.5])
eval_result["mAPr0.7"] = mAPr(eval_result["per_categ_jaccard"], [0.7])
eval_result["mAPr-vol"] = mAPr(eval_result["per_categ_jaccard"], np.linspace(0.1, 0.9, 9))
return eval_result
return _img, _target
def __str__(self):
return 'SBDSegmentation(split=' + str(
self.split) + ', area_thres=' + str(self.area_thres) + ')'
if __name__ == '__main__':
import matplotlib.pyplot as plt
import dataloaders.helpers as helpers
import torch
import torchvision.transforms as transforms
import dataloaders.custom_transforms as tr
transform = transforms.Compose([tr.ToTensor()])
dataset = SBDSegmentation(transform=transform)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=True,
num_workers=2)
for i, data in enumerate(dataloader):
plt.figure()
plt.imshow(helpers.tens2image(data['image']) / 255)
plt.figure()
plt.imshow(helpers.tens2image(data['gt'])[:, :, 0])
if i == 10:
break
plt.show(block=True)
if __name__ == '__main__':
import matplotlib.pyplot as plt
import dataloaders.helpers as helpers
import torch
import dataloaders.custom_transforms as tr
from torchvision import transforms
transform = transforms.Compose([tr.ToTensor()])
dataset = VOCSegmentation(split=['train', 'val'],
transform=transform,
retname=True)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=0)
for i, sample in enumerate(dataloader):
plt.figure()
overlay = helpers.overlay_mask(
helpers.tens2image(sample["image"]) / 255.,
np.squeeze(helpers.tens2image(sample["gt"])))
plt.imshow(overlay)
plt.title(dataset.category_names[sample["meta"]["category"][0]])
if i == 3:
break
plt.show(block=True)